The invention relates generally to Condition Based Maintenance (CBM), and in particular to advanced condition monitoring of assets using smart sensors to improve asset maintenance agility and responsiveness, increase operational availability of the asset, and reduce life-cycle total ownership costs of the asset.
There are billions of dollars invested in fixed-plant equipment performing vital and time-critical functions supporting industrial and infrastructure activities. It is essential that degradation in these equipment be expeditiously identified in order to isolate or repair the ailing equipment before they fail and severely impact the efficiency of the system of which they are a part or, even more seriously, cause physical damage that is spread significantly beyond the failed equipment.
Maintenance has evolved over the years from purely reacting to equipment breakdowns (corrective maintenance), to performing time-based preventive maintenance, to today's emphasis on the need to perform maintenance based on the condition of the system/asset (condition based maintenance). Anomaly detection is a critical task in equipment monitoring, fault diagnostics and system prognostics. It involves monitoring changes to the system state to detect faulty behavior. Early detection of anomalies will allow for timely maintenance actions to be taken before a potential fault progresses, causing secondary damage and equipment downtime. Prior approaches for anomaly detection commonly utilize univariate techniques to detect changes in the measurement of individual sensors. However, typically a system's state is characterized by the interactions and inter-relationships between the various sensor measurements considered together.
There is a growing need for Condition Based Maintenance (CBM) to improve maintenance agility and responsiveness, increase operational availability, and reduce life-cycle total ownership costs.